Automated parameterization of land surface process models using fuzzy logic

All land surface process models require parameters that are proxies for spatial processes that are impractical or impossible to measure. Recent developments in model parameter estimation theory suggest that information obtained from calibrating such models is inherently uncertain in nature. As a consequence, identification of optimum parameter values is often highly non-specific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the sub-canopy controls on transpiration in a land surface process model demonstrates that objective estimates of parameter values and expected ranges of predictions can be obtained with suitable choices for objective functions. An iterative refinement in parameter estimates was possible with conditional sampling techniques. The automated approach was able to correctly identify parameter tradeoffs such that two strongly different sets of parameters could produce almost equal daily average canopy transpiration for two upland deciduous tree species. The approach failed to correctly identify such idealized model parameter tradeoffs for upland conifer and wetland tree species. There remains a need for expert knowledge to choose among a range of parameter values. Due to its computational simplicity and flexibility, the framework presented and its associated algorithms can easily be embedded within a GIS to support the parameterization of land surface models. However, the core analytical tools would need to be augmented by human expertise or some form of expert system to reliably generate model parameters.